► The underwater domain is a dangerous and complex environment for human divers. Often, divers have to monitor their own life support systems as they navigate…
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▼ The underwater domain is a dangerous and complex environment for human divers. Often, divers have to monitor their own life support systems as they navigate to the work site or operate dangerous machinery. Military divers have to navigate for extended periods of time without surfacing or without using localization techniques that might give away their positions. Human divers have operated under these harsh conditions for decades with few advancements in technology. In fact, a diver performs the basic task of navigation by aligning the body with a compass and counting leg kicks (i.e., human-oriented dead-reckoning). It is proposed that an Underwater Robotic Assistant (UWRA) will improve the efficiency and safety of the diver's underwater operations by providing several key capabilities. For example, the UWRA can provide navigation assistance, ferry tools from the surface, enter structures too dangerous for human divers, and carry hazardous materials. However, in unstructured environments, underwater robots are limited in their ability to localize and track a human diver at the resolution required to enable diver-robot interactions. Optical cameras can be rendered useless by the turbidity of the water, localizing radio signals do not propagate well through the water medium, and acoustic positioning systems can be expensive to deploy. We propose that by developing novel 2D imaging sonar processing techniques, an underwater robot can detect, track, and trail a human diver. The objective of this research is to detect and track human divers in 2D imaging sonar data. While the physical properties of sonar allow it to detect objects at longer ranges than optical cameras in underwater scenarios, it is plagued with noise and multi-path propagation. Also, when a diver is ensonified with a 2D imaging sonar, a fragmented acoustic reflection is returned. The fact that a single object can produce multiple returns means that tracking the human diver cannot be solved by applying traditional multiple hypothesis tracking algorithms, which operate on the assumption of each object generating only a single measurement. To overcome the sonar noise and multiple fragmented returns, we developed a novel adaptive thresholding algorithm and a hierarchical multiple object tracking algorithm. While the Kalman filter is extensively used in our tracking algorithms, we developed a novel method for adaptively modifying the Kalman filter's measurement matrix to track objects that generate multiple measurements.
Advisors/Committee Members: Howard, Ayanna M. (advisor), Collins, Thomas R. (committee member), Egerstedt, Magnus B. (committee member), Balch, Tucker (committee member), West, Michael E. (committee member).

► Multiagent simulation (MAS) can be a valuable tool for biologists and ethologists studying collective animal behavior. However, constructing models for simulation is often a time-consuming…
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▼ Multiagent simulation (MAS) can be a valuable tool for biologists and ethologists studying collective animal behavior. However, constructing models for simulation is often a time-consuming manual task. Current state-of-the-art multitarget tracking algorithms can now provide high accuracy, high density tracking data of groups of research animals. Techniques from machine learning should be able to leverage the wealth of information such data provides in order to automatically find good models of collective behavior that can be executed in simulation. However, models trained using traditional single-step loss functions can lead to behaviors that are qualitatively dissimilar to the target behavior, while Expectation Maximization (EM) methods computed over full trajectories are subject to local suboptima. These problems are particularly compounded in the case of multiple interacting agents, as in collective behaviors which are the focus of this dissertation. It is useful to examine two specific categories of collective behavior: stochastic behaviors and stateful behaviors, to illustrate the need for new learning techniques and evaluation criteria. Stochastic behaviors can be captured by modeling the distribution of behavior, while models with behavioral state can capture more complex behaviors that switch between multiple low-level modes. The schooling behavior of fish, and the foraging behavior of ants provide examples through which new models and learning methods are explored, and this exploration leads naturally to a novel quantitative evaluation framework based on the statistical similarity between the observed behaviors called Behavioral Divergence. This dissertation describes methods for building and learning executable models, the trade-offs between their strengths and weaknesses, introduces a novel quantitative evaluation framework called Behavioral Divergence that complements existing approaches, and experimentally compares Behavioral Divergence with predictive performance.
Advisors/Committee Members: Balch, Tucker (advisor), Boots, Byron (committee member), Egerstedt, Magnus (committee member), Turk, Greg (committee member), Luke, Sean (committee member).

► This dissertation aims to demonstrate how perceptual goal specifications may be used as alternative representations for specifying domain-specific reward functions for reinforcement learning. The works…
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▼ This dissertation aims to demonstrate how perceptual goal specifications may be used as alternative representations for specifying domain-specific reward functions for reinforcement learning. The works outlined in this document aim to validate the following thesis statement: Employing perceptual goal specifications for goal-directed tasks: is as straightforward as specifying domain-specific rewards; is a more general representation for tasks; and equally enables task completion. We describe various approaches for specifying goals visually and how we may compute rewards and learn policies directly from these representations. Chapter 4 introduces Perceptual Reward Functions and describes how we can utilize a hand-defined similarity metric to enable learning from goals that are different from an agent’s. Chapter 5 introduces Cross-Domain Perceptual Reward Functions and describes how we can learn a reward function for cross-domain goal specifications. Chapter 6 introduces Perceptual Value Functions and describes how we can learn a value function from sequences of expert observations without access to ground-truth actions. Chapter 7 introduces Latent Policy Networks and describes how we can learn a policy from sequences of expert observations without access to ground-truth actions. The remaining chapters motivate and provide background for this dissertation and outline a plan for future research.
Advisors/Committee Members: Isbell, Charles L (advisor), Balch, Tucker (committee member), Chernova, Sonia (committee member), Riedl, Mark (committee member), Abbeel, Pieter (committee member).

► This research address the problem of inferring, through Radio-Frequency Identification (RFID) tracking data, the graph structures underlying social interactions in a group of rhesus macaques…
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▼ This research address the problem of inferring, through Radio-Frequency Identification (RFID) tracking data, the graph structures underlying social interactions in a group of rhesus macaques (a species of monkey). These social interactions are considered as independent affiliative and dominative components and are characterized by a variety of visual and auditory displays and gestures. Social structure in a group is an important indicator of its members’ relative level of access to resources and has interesting implications for an individual’s health. Automatic inference of the social structure in an animal group enables a number of important capabilities, including:
1. A verifiable measure of how the social structure is affected by an intervention such as a change in the environment, or the introduction of another animal, and
2. A potentially significant reduction in person hours normally used for assessing these changes.
The behaviors of interest in the context of this research are those definable using the macaques’ spatial (x,y,z) position and motion inside an enclosure. Periods of time spent in close proximity with other group members are considered to be events of passive interaction and are used in the calculation of an Affiliation Matrix. This represents the strength of undirected interaction or tie-strength between individual animals. Dominance is a directed relation that is quantified using a heuristic for the detection of withdrawal and displacement behaviors. The results of an analysis based on these approaches for a group of 6 male monkeys that were tracked over a period of 60 days at the Yerkes Primate Research Center are presented in this Thesis.
Advisors/Committee Members: Balch, Tucker (advisor), Clements, Mark (advisor), Dovrolis, Constantine (committee member), Egerstedt, Magnus (committee member).

► Many applications for field robots can benefit from large numbers of robots, especially applications where the objective is for the robots to cover or explore…
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▼ Many applications for field robots can benefit from large numbers of robots, especially applications where the objective is for the robots to cover or explore a region. A key enabling technology for robust autonomy in these teams of small and cheap robots is the development of collaborative perception to account for the shortcomings of the small and cheap sensors on the robots. In this dissertation, I present DDF-SAM to address the decentralized data fusion (DDF) inference problem with a smoothing and mapping (SAM) approach to single-robot mapping that is online, scalable and consistent while supporting a variety of sensing modalities. The DDF-SAM approach performs fully decentralized simultaneous localization and mapping in which robots choose a relevant subset of variables from their local map to share with neighbors. Each robot summarizes their local map to yield a density on exactly this chosen set of variables, and then distributes this summarized map to neighboring robots, allowing map information to propagate throughout the network. Each robot fuses summarized maps it receives to yield a map solution with an extended sensor horizon. I introduce two primary variations on DDF-SAM, one that uses a batch nonlinear constrained optimization procedure to combine maps, DDF-SAM 1.0, and one that uses an incremental solving approach for substantially faster performance, DDF-SAM 2.0. I validate these systems using a combination of real-world and simulated experiments. In addition, I evaluate design trade-offs for operations within DDF-SAM, with a focus on efficient approximate map summarization to minimize communication costs.
Advisors/Committee Members: Dellaert, Frank (advisor), Howard, Ayanna M. (committee member), Egerstedt, Magnus (committee member), Christensen, Henrik I. (committee member), Balch, Tucker (committee member), Roumeliotis, Stergios I. (committee member).

► In this thesis, I study the computational advantages of the allocentric represen- tation as compared to the egocentric representation for autonomous local navigation. Whereas in…
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▼ In this thesis, I study the computational advantages of the allocentric represen- tation as compared to the egocentric representation for autonomous local navigation. Whereas in the allocentric framework, all variables of interest are represented with respect to a coordinate frame attached to an object in the scene, in the egocentric one, they are always represented with respect to the robot frame at each time step.
In contrast with well-known results in the Simultaneous Localization and Mapping literature, I show that the amounts of nonlinearity of these two representations, where poses are elements of Lie-group manifolds, do not affect the accuracy of Gaussian- based filtering methods for perception at both the feature level and the object level. Furthermore, although these two representations are equivalent at the object level, the allocentric filtering framework is better than the egocentric one at the feature level due to its advantages in the marginalization process. Moreover, I show that the object- centric perspective, inspired by the allocentric representation, enables novel linear- time filtering algorithms, which significantly outperform state-of-the-art feature-based filtering methods with a small trade-off in accuracy due to a low-rank approximation. Finally, I show that the allocentric representation is also better than the egocentric representation in Model Predictive Control for local trajectory planning and obstacle avoidance tasks.
Advisors/Committee Members: Dellaert, Frank (advisor), Arkin, Ronald C. (committee member), Tsiotras, Panagiotis (committee member), Balch, Tucker (committee member), Sibley, Gabe (committee member).

▼ We analyze spatio-temporal routing under various constraints specific to multi-robot applications. Spatio-temporal routing requires multiple robots to visit spatial locations at specified time instants, while optimizing certain criteria like the total distance traveled, or the total energy consumed. Such a spatio-temporal concept is intuitively demonstrable through music (e.g. a musician routes multiple fingers to play a series of notes on an instrument at specified time instants). As such, we showcase much of our work on routing through this medium. Particular to robotic applications, we analyze constraints like maximum velocities that the robots cannot exceed, and information-exchange networks that must remain connected. Furthermore, we consider a notion of heterogeneity where robots and spatial locations are associated with multiple skills, and a robot can visit a location only if it has at least one skill in common with the skill set of that location. To extend the scope of our work, we analyze spatio-temporal routing in the context of a distributed framework, and a dynamic environment.
Advisors/Committee Members: Egerstedt, Magnus (advisor), Zhang, Fumin (committee member), Howard, Ayanna M. (committee member), Reveliotis, Spiridon (committee member), Balch, Tucker (committee member).

► As robots become more and more prevalent in our everyday life, making sure that our interactions with them are natural and satisfactory is of paramount…
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▼ As robots become more and more prevalent in our everyday life, making sure that our interactions with them are natural and satisfactory is of paramount importance. Given the propensity of humans to treat machines as social actors, and the integral role affect plays in human life, providing robots with affective responses is a step towards making our interaction with them more intuitive. To the end of promoting more natural, satisfying and effective human-robot interaction and enhancing robotic behavior in general, an integrative framework of time-varying affective robotic behavior was designed and implemented on a humanoid robot. This psychologically inspired framework (TAME) encompasses 4 different yet interrelated affective phenomena: personality Traits, affective Attitudes, Moods and Emotions. Traits determine consistent patterns of behavior across situations and environments and are generally time-invariant; attitudes are long-lasting and reflect likes or dislikes towards particular objects, persons, or situations; moods are subtle and relatively short in duration, biasing behavior according to favorable or unfavorable conditions; and emotions provide a fast yet short-lived response to environmental contingencies. The software architecture incorporating the TAME framework was designed as a stand-alone process to promote platform-independence and applicability to other domains.
In this dissertation, the effectiveness of affective robotic behavior was explored and evaluated in a number of human-robot interaction studies with over 100 participants. In one of these studies, the impact of Negative Mood and emotion of Fear was assessed in a mock-up search-and-rescue scenario, where the participants found the robot expressing affect more compelling, sincere, convincing and "conscious" than its non-affective counterpart. Another study showed that different robotic personalities are better suited for different tasks: an extraverted robot was found to be more welcoming and fun for a task as a museum robot guide, where an engaging and gregarious demeanor was expected; whereas an introverted robot was rated as more appropriate for a problem solving task requiring concentration. To conclude, multi-faceted robotic affect can have far-reaching practical benefits for human-robot interaction, from making people feel more welcome where gregariousness is expected to making unobtrusive partners for problem solving tasks to saving people's lives in dangerous situations.
Advisors/Committee Members: Arkin, Ronald (Committee Chair), Balch, Tucker (Committee Member), Fisk, Arthur (Committee Member), Howard, Ayanna (Committee Member), Jackson, Melody (Committee Member).

► Experience forms the basis of learning. It is crucial in the development of human intelligence, and more broadly allows an agent to discover and learn…
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▼ Experience forms the basis of learning. It is crucial in the development of human intelligence, and more broadly allows an agent to discover and learn about the world around it. Although experience is fundamental to learning, it is costly and time-consuming to obtain. In order to speed this process up, humans in particular have developed communication abilities so that ideas and knowledge can be shared without requiring first-hand experience.
Consider the same need for knowledge sharing among robots. Based on the recent growth of the field, it is reasonable to assume that in the near future there will be a collection of robots learning to perform tasks and gaining their own experiences in the world. In order to speed this learning up, it would be beneficial for the various robots to share their knowledge with each other. In most cases, however, the communication of knowledge among humans relies on the existence of similar sensory and motor capabilities. Robots, on the other hand, widely vary in perceptual and motor apparatus, ranging from simple light sensors to sophisticated laser and vision sensing.
This dissertation defines the problem of how heterogeneous robots with widely different capabilities can share experiences gained in the world in order to speed up learning. The work focus specifically on differences in sensing and perception, which can be used both for perceptual categorization tasks as well as determining actions based on environmental features. Motivating the problem, experiments first demonstrate that heterogeneity does indeed pose a problem during the transfer of object models from one robot to another. This is true even when using state of the art object recognition algorithms that use SIFT features, designed to be unique and reproducible.
It is then shown that the abstraction of raw sensory data into intermediate categories for multiple object features (such as color, texture, shape, etc.), represented as Gaussian Mixture Models, can alleviate some of these issues and facilitate effective knowledge transfer. Object representation, heterogeneity, and knowledge transfer is framed within Gärdenfors' conceptual spaces, or geometric spaces that utilize similarity measures as the basis of categorization. This representation is used to model object properties (e.g. color or texture) and concepts (object categories and specific objects).
A framework is then proposed to allow heterogeneous robots to build models of their differences with respect to the intermediate representation using joint interaction in the environment. Confusion matrices are used to map property pairs between two heterogeneous robots, and an information-theoretic metric is proposed to model information loss when going from one robot's representation to another. We demonstrate that these metrics allow for cognizant failure, where the robots can ascertain if concepts can or cannot be shared, given their respective capabilities.
After this period of joint interaction, the learned models are used to facilitate…
Advisors/Committee Members: Arkin, Ronald (Committee Chair), Balch, Tucker (Committee Member), Collins, Thomas (Committee Member), Goel, Ashok (Committee Member), Isbell, Charles (Committee Member).

► Many biological systems are known to accomplish complex tasks in a decentralized, robust, and scalable manner - characteristics that are desirable to the coordination of…
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▼ Many biological systems are known to accomplish complex tasks in a decentralized, robust, and scalable manner - characteristics that are desirable to the coordination of engineered systems as well. Inspired by nature, we produce coordination strategies for a network of heterogenous agents and in particular, we focus on intelligent collective systems. Bottlenose dolphins and African lions are examples of intelligent collective systems since they exhibit sophisticated social behaviors and effortlessly transition between functionalities. Through preferred associations, specialized roles, and self-organization, these systems forage prey, form alliances, and maintain sustainable group sizes. In this thesis, we take a three-phased approach to bioinspiration: in the first phase, we produce agent-based models of specific social behaviors observed in nature. The goal of these models is to capture the underlying biological phenomenon, yet remain simple so that the models are amenable to analysis. In the second phase, we produce bio-inspired algorithms that are based on the simple biological models produced in the first phase. Moreover, these algorithms are developed in the context of specific coordination tasks, e.g., the multi-agent foraging task. In the final phase of this work, we tailor these algorithms to produce coordination strategies that are ready to be deployed in target applications.
Advisors/Committee Members: Egerstedt, Magnus (Committee Chair), Anderson, David (Committee Member), Balch, Tucker (Committee Member), Howard, Ayanna (Committee Member), Vela, Patricio (Committee Member).

▼ Like computer architects, robot designers must address multiple, possibly competing, requirements by balancing trade-offs in terms of processing, memory, communication, and energy to satisfy design objectives. However, robot architects currently lack the design guidelines, organizing principles, rules of thumb, and tools that computer architects rely upon. This thesis takes a step in this direction, by analyzing the roles of heterogeneity and distribution in robot systems architecture.
This thesis takes a systems architecture approach to the design of robot systems, and in particular, investigates the use of distributed, heterogeneous platforms to exploit locality in robot systems design. We show how multiple, distributed heterogeneous platforms can serve as general purpose robot systems for three distinct domains with different design objectives: increasing availability in a search and rescue mission, increasing flexibility and ease-of-use for a personal educational robot, and decreasing the computation and sensing resources necessary for navigation and foraging tasks.
Advisors/Committee Members: Balch, Tucker (Committee Chair), Christensen, Henrik (Committee Member), Guzdial, Mark (Committee Member), Schwan, Karsten (Committee Member), Sukhatme, Gaurav (Committee Member).

▼ The conventional Mini and Large scale Unmanned Aerial Vehicle systems span anywhere from approximately 12 inches to 12 feet; endowing them with larger propulsion systems, batteries/fuel-tanks, which in turn provide ample power reserves for long-endurance flights, powerful actuators, on-board avionics, wireless telemetry etc. The limitations thus imposed become apparent when shifting to Micro Aerial Vehicles (MAVs) and trying to equip them with equal or near-equal flight endurance, processing, sensing and communication capabilities, as their larger scale cousins. The conventional MAV as outlined by The Defense Advanced Research Projects Agency (DARPA) is a vehicle that can have a maximum dimension of 6 inches and weighs no more than 100 grams. Under these tight constraints, the footprint, weight and power reserves available to on-board avionics and actuators is drastically reduced; the flight time and payload capability of MAVs take a massive plummet in keeping with these stringent size constraints. However, the demand for micro flying robots is increasing rapidly.
The applications that have emerged over the years for MAVs include search&rescue operations for trapped victims in natural disaster succumbed urban areas; search&reconnaissance in biological, radiation, natural disaster/hazard succumbed/prone areas; patrolling&securing home/office/building premises/urban areas. VTOL capable rotary and fixed wing flying vehicles do not scale down to micro sized levels, owing to the severe loss in aerodynamic efficiency associated with low Reynolds number physics on conventional airfoils; whereas, present state of the art in flapping wing designs lack in one or more of the minimum qualities required from an MAV: Appreciable flight time, appreciable payload capacity for on-board sensors/telemetry and 6DoF hovering/VTOL performance. This PhD. work is directed towards overcoming these limitations.
Firstly, this PhD thesis presents the advent of a novel Quad-Wing MAV configuration (called the QV). The Four-Wing configuration is capable of performing all 6DoF flight maneuvers including VTOL. The thesis presents the design, conception, simulation study and finally hardware design/development of the MAV.
Secondly, this PhD thesis proves and demonstrates significant improvement in on-board Energy-Harvesting resulting in increased flight times and payload capacities of the order of even 200%-400% and more.
Thirdly, this PhD thesis defines a new actuation principle called, Fixed Frequency, Variable Amplitude (FiFVA). It is demonstrated that by the use of passive elastic members on wing joints, a further significant increase in energy efficiency and consequently reduction in input power requirements is observed. An actuation efficiency increase of over 100% in many cases is possible. The natural evolution of actuation development led to invention of two novel actuation systems to illustrate the FiFVA actuation principle and consequently show energy savings and flapping efficiency improvement.
Lastly, but not in the least,…
Advisors/Committee Members: Vachtsevanos, George (Committee Chair), Balch, Tucker (Committee Member), Book, Wayne (Committee Member), Egerstedt, Magnus (Committee Member), Howard, Ayanna (Committee Member).

► In the future, robots will expand from industrial and research applications to the home. Domestic service robots will work in the home to perform useful…
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▼ In the future, robots will expand from industrial and research applications to the home. Domestic service robots will work in the home to perform useful tasks such as object retrieval, cleaning, organization, and security. The tireless support of these systems will not only enable able bodied people to avoid mundane chores; they will also enable the elderly to remain independent from institutional care by providing service, safety, and companionship. Robots will need to understand the relationship between objects and their environments to perform some of these tasks. Structured indoor environments are organized according to architectural guidelines and convenience for their residents. Utilizing this information makes it possible to predict the location of objects. Conversely, one can also predict the function of a room from the detection of a few objects within a given space.
This thesis introduces a framework for combining object permanence and context called the probabilistic cognitive model. This framework combines reasoning about spatial extent of places and the identity of objects and their relationships to one another and to the locations where they appear. This type of reasoning takes into account the context in which objects appear to determine their identity and purpose. The probabilistic cognitive model combines a mapping system called OmniMapper with a conditional random field probabilistic model for context representation. The conditional random field models the dependencies between location and identity in a real-world domestic environment. This model is used by mobile robot systems to predict the effects of their actions during autonomous object search tasks in unknown environments.
Advisors/Committee Members: Christensen, Henrik (Committee Chair), Balch, Tucker (Committee Member), Daniilidis, Kostas (Committee Member), Dellaert, Frank (Committee Member), Howard, Ayanna (Committee Member).

► Agents in most types of societies use information about potential partners to determine whether to form mutually beneficial partnerships. We can say that when this…
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▼ Agents in most types of societies use information about potential partners to determine whether to form mutually beneficial partnerships. We can say that when this information is used to decide to form a partnership that one agent trusts another, and when agents work together for mutual benefit in a partnership, we refer to this as a form of cooperation. Current multi-robot teams typically have the team's goals either explicitly or implicitly encoded into each robot's utility function and are expected to cooperate and perform as designed. However, there are many situations in which robots may not be interested in full cooperation, or may not be capable of performing as expected. In addition, the control strategy for robots may be fixed with no mechanism for modifying the team structure if teammate performance deteriorates. This dissertation investigates the application of trust to multi-robot teams. This research also addresses the problem of how cooperation can be enabled through the use of incentive mechanisms. We posit a framework wherein robot teams may be formed dynamically, using models of trust. These models are used to improve performance on the team, through evolution of the team dynamics. In this context, robots learn online which of their peers are capable and trustworthy to dynamically adjust their teaming strategy.
We apply this framework to multi-robot task allocation and patrolling domains and show that performance is improved when this approach is used on teams that may have poorly performing or untrustworthy members. The contributions of this dissertation include algorithms for applying performance characteristics of individual robots to task allocation, methods for monitoring performance of robot team members, and a framework for modeling trust of robot team members. This work also includes experimental results gathered using simulations and on a team of indoor mobile robots to show that the use of a trust model can improve performance on multi-robot teams in the patrolling task.
Advisors/Committee Members: Christensen, Henrik I. (advisor), Balch, Tucker (committee member), Dellaert, Frank (committee member), Egerstedt, Magnus (committee member), Parker, Lynne (committee member).

► Behavioral research involves the study of the behaviors of one or more agents (often animals) in order to better understand the agents' thoughts and actions.…
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▼ Behavioral research involves the study of the behaviors of one or more agents (often animals) in order to better understand the agents' thoughts and actions. Identifying subject movements and behaviors based upon those movements is a critical, time-consuming step in behavioral research. To successfully perform behavior analysis, three goals must be met. First, the agents of interest are observed, and their movements recorded. Second, each individual must be uniquely identified. Finally, behaviors must be identified and recognized. I explore a system that can uniquely identify and track agents, then use these tracks to automatically build behavioral models and recognize similar behaviors in the future.
I address the tracking and identification problems using a combination of laser range finders, active RFID sensors, and probabilistic models for real-time tracking. The laser range component adds environmental flexibility over vision based systems, while the RFID tags help disambiguate individual agents. The probabilistic models are important to target identification during the complex interactions with other agents of similar appearance. In addition to tracking, I present work on automatic methods for generating behavioral models based on supervised learning techniques using the agents' tracked data. These models can be used to classify new tracked data and identify the behavior exhibited by the agent, which can then be used to help automate behavior analysis.
Advisors/Committee Members: Balch, Tucker (Committee Chair), Essa, Irfan (Committee Member), Isbell, Charles (Committee Member), Starner, Thad (Committee Member), Wallen, Kim (Committee Member).

► One key to more effective cooperative interaction in a multi-robot team is the ability to understand the behavior and intent of other robots. Observed teammate…
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▼ One key to more effective cooperative interaction in a multi-robot team is the ability to understand the behavior and intent of other robots. Observed teammate action sequences can be learned to perform trajectory recognition which can be used to determine their current task. Previously, we have applied behavior histograms, hidden Markov models (HMMs), and conditional random fields (CRFs) to perform trajectory recognition as an approach to task monitoring in the absence of commu- nication. To demonstrate trajectory recognition of various autonomous vehicles, we used trajectory-based techniques for model generation and trajectory discrimination in experiments using actual data. In addition to recognition of trajectories, we in- troduced strategies, based on the honeybee’s waggle dance, in which cooperating autonomous teammates could leverage recognition during periods of communication loss. While the recognition methods were able to discriminate between the standard trajectories performed in a typical survey mission, there were inaccuracies and delays in identifying new trajectories after a transition had occurred. Inaccuracies in recog- nition lead to inefficiencies as cooperating teammates acted on incorrect data. We then introduce the Trajectory Adaptation for Recognition (TAR) framework which seeks to directly address difficulties in recognizing the trajectories of autonomous vehicles by modifying the trajectories they follow to perform them. Optimization techniques are used to modify the trajectories to increase the accuracy of recognition while also improving task objectives and maintaining vehicle dynamics. Experiments are performed which demonstrate that using trajectories optimized in this manner lead to improved recognition accuracy.
Advisors/Committee Members: Balch, Tucker R. (advisor), Egerstedt, Magnus (committee member), Christensen, Henrik I. (committee member), Collins, Thomas R. (committee member), Rehg, James M. (committee member), Weiss, Lora G. (committee member).

► A fundamental requirement of any autonomous robot system is the ability to predict the affordances of its environment, which define how the robot can interact…
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▼ A fundamental requirement of any autonomous robot system is the ability to predict the affordances of its environment, which define how the robot can interact with various objects. In this dissertation, we demonstrate that the conventional direct perception approach can indeed be applied to the task of training robots to predict affordances, but it does not consider that objects can be grouped into categories such that objects of the same category have similar affordances. Although the connection between object categorization and the ability to make predictions of attributes has been extensively studied in cognitive science research, it has not been systematically applied to robotics in learning to predict a number of affordances from recognizing object categories.
We develop a computational framework of learning and predicting affordances where a robot explicitly learns the categories of objects present in its environment in a partially supervised manner, and then conducts experiments to interact with the objects to both refine its model of categories and the category-affordance relationships. In comparison to the direct perception approach, we demonstrate that categories make the affordance learning problem scalable, in that they make more effective use of scarce training data and support efficient incremental learning of new affordance concepts. Another key aspect of our approach is to leverage the ability of a robot to perform experiments on its environment and thus gather information independent of a human trainer. We develop the theoretical underpinnings of category-based affordance learning and validate our theory on experiments with physically-situated robots. Finally, we refocus the object categorization problem of computer vision back to the theme of autonomous agents interacting with a physical world consisting of categories of objects. This enables us to reinterpret and extend the Gluck-Corter category utility function for the task of learning categorizations for affordance prediction.
Advisors/Committee Members: Rehg, James M. (Committee Chair), Bobick, Aaron (Committee Co-Chair), Balch, Tucker (Committee Member), Christensen, Henrik I. (Committee Member), Pietro Perona (Committee Member).

► The ability to use tools is one of the hallmarks of intelligence. Tool use is fundamental to human life and has been for at least…
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▼ The ability to use tools is one of the hallmarks of intelligence. Tool use is fundamental to human life and has been for at least the last two million years. We use tools to extend our reach, to amplify our physical strength, and to achieve many other tasks. A large number of animals have also been observed to use tools. Despite the widespread use of tools in the animal world, however, studies of autonomous robotic tool use are still rare.
This dissertation examines the problem of autonomous tool use in robots from the point of view of developmental robotics. Therefore, the main focus is not on optimizing robotic solutions for specific tool tasks but on designing algorithms and representations that a robot can use to develop tool-using abilities.
The dissertation describes a developmental sequence/trajectory that a robot can take in order to learn how to use tools autonomously. The developmental sequence begins with learning a model of the robot's body since the body is the most consistent and predictable part of the environment. Specifically, the robot learns which perceptual features are associated with its own body and which with the environment. Next, the robot can begin to identify certain patterns exhibited by the body itself and to learn a robot body schema model which can also be used to encode goal-oriented behaviors. The robot can also use its body as a well defined reference frame from which the properties of environmental objects can be explored by relating them to the body. Finally, the robot can begin to relate two environmental objects to one another and to learn that certain actions with the first object can affect the second object, i.e., the first object can be used as a tool.
The main contributions of the dissertation can be broadly summarized as follows: it demonstrates a method for autonomous self-detection in robots; it demonstrates a model for extendable robot body schema which can be used to achieve goal-oriented behaviors, including video-guided behaviors; it demonstrates a behavior-grounded method for learning the affordances of tools which can also be used to solve tool-using tasks.
Advisors/Committee Members: Arkin, Ronald (Committee Chair), Balch, Tucker (Committee Member), Bobick, Aaron (Committee Member), Isbell, Charles (Committee Member), Lipkin, Harvey (Committee Member).

► With the growth of successes in pattern recognition and signal processing, mobile robot applications today are increasingly equipping their hardware with microphones to improve the…
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▼ With the growth of successes in pattern recognition and signal processing, mobile robot applications today are increasingly equipping their hardware with microphones to improve the set of available sensory information. However, if the robot, and therefore the microphone, ends up in a poor location acoustically, then the data will remain noisy and potentially useless for accomplishing the required task. This is compounded by the fact that there are many bad acoustic locations through which a robot is likely to pass, and so the results from auditory sensors often remain poor for much of the task.
The movement of the robot, though, can also be an important tool for overcoming these problems, a tool that has not been exploited in the traditional signal processing community. Robots are not limited to a single location as are traditionally placed microphones, nor are they powerless over to where they will be moved as with wearable computers. If there is a better location available for performing its task, a robot can navigate to that location under its own power. Furthermore, when deciding where to move, robots can develop complex models of the environment. Using an array of sensors, a mobile robot can build models of sound flow through an area, picking from those models the paths most likely to improve performance of an acoustic application.
In this dissertation, we address the question of how to exploit robotic movement. Using common sensors, we present a collection of tools for gathering information about the auditory scene and incorporating that information into a general framework for acoustical awareness. Thus equipped, robots can make intelligent decisions regarding control strategies to enhance their performance on the underlying acoustic application.
Advisors/Committee Members: Arkin, Ronald (Committee Chair), Anderson, Dave (Committee Member), Balch, Tucker (Committee Member), Dellaert, Frank (Committee Member), Starner, Thad (Committee Member).

► Topological maps are light-weight, graphical representations of environments that are scalable and amenable to symbolic manipulation. Thus, they are well- suited for basic robot navigation…
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▼ Topological maps are light-weight, graphical representations of environments
that are scalable and amenable to symbolic manipulation. Thus, they are well-
suited for basic robot navigation applications, and also provide a representational
basis for the procedural and semantic information needed for higher-level robotic
tasks. However, their widespread use has been impeded in part by the lack of
reliable, general purpose algorithms for their construction.
In this dissertation, I present a probabilistic framework for the construction of
topological maps that addresses topological ambiguity, is failure-aware, computa-
tionally efficient, and can incorporate information from various sensing modalities.
The framework addresses the two major problems of topological mapping, namely
topological ambiguity and landmark detection.
The underlying idea behind overcoming topological ambiguity is that the com-
putation of the Bayesian posterior distribution over the space of topologies is an
effective means of quantifying this ambiguity, caused due to perceptual aliasing
and environment variability. Since the space of topologies is combinatorial, the
posterior on it cannot be computed exactly. Instead, I introduce the concept of
Probabilistic Topological Maps (PTMs), a sample-based representation that ap-
proximates the posterior distribution over topologies given the available sensor
measurements. Sampling algorithms for the efficient computation of PTMs are
described.
The PTM framework can be used with a wide variety of landmark detection
schemes under mild assumptions. As part of the evaluation, I describe a novel
landmark detection technique that makes use of the notion of "surprise" in mea-
surements that the robot obtains, the underlying assumption being that landmarks
are places in the environment that generate surprising measurements. The com-
putation of surprise in a Bayesian framework is described and applied to various
sensing modalities for the computation of PTMs.
The PTM framework is the first instance of a probabilistic technique for topo-
logical mapping that is systematic and comprehensive. It is especially relevant
for future robotic applications which will need a sparse representation capable of
accomodating higher level semantic knowledge. Results from experiments in real environments demonstrate that the framework can accomodate diverse sensors such
as camera rigs and laser scanners in addition to odometry. Finally, results are pre-
sented using various landmark detection schemes besides the surprise-based one.
Advisors/Committee Members: Dellaert, Frank (Committee Chair), Balch, Tucker (Committee Member), Christensen, Henrik (Committee Member), Kuipers, Benjamin (Committee Member), Rehg, Jim (Committee Member).

► Recently in robotics, substantial efforts have been invested on critical applications such as military, nursing, and search-and-rescue. These applications are critical in a sense that…
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▼ Recently in robotics, substantial efforts have been invested on critical applications such as military, nursing, and search-and-rescue. These applications are critical in a sense that the robots may directly deal with human lives in life-or-death situations, and they are therefore required to make highly intelligent decisions as rapidly as possible. The intelligence we are looking for in this type of situations is proactiveness: the ability to anticipate as well as improvise.
Anticipation here means that the robot can assess the current situation, predict the future consequence of the situation, and execute an action to have desired outcome based on the determined assessment and prediction. On the other hand, improvisation is performed when the consequence of the situation is not fully known. In other words, it is the ability to deal with a novel situation based on knowledge or skill being acquired before.
In this presentation, we introduce a biologically inspired computational model of proactive intelligent behavior for robots. Integrating multiple levels of machine learning techniques such as temporal difference learning, instance-based learning, and partially observable Markov decision process, aggregated episodic memories are processed in order to accomplish anticipation as well as improvisation. How this model can be implemented within a software architectural framework and integrated into a physically realized robotic system is also explained. The experimental results using a real robot and high fidelity 3D simulators are then presented in order to help us understand how extended experience of a robot influences its ability to behave proactively.
Advisors/Committee Members: Arkin, Ronald (Committee Chair), Balch, Tucker (Committee Member), Dellaert, Frank (Committee Member), Potter, Steve (Committee Member), Ram, Ashwin (Committee Member).

► This work develops optimal assembly sequences for modular building blocks. The underlying concept is that an automated device could take a virtual shape such as…
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▼ This work develops optimal assembly sequences for modular building blocks. The underlying concept is that an automated device could take a virtual shape such as a CAD file, and automatically decide how to physically build the shape using simple, identical building blocks. This entails deciding where to place blocks inside the shape and generating an efficient assembly sequence that a robot could use to build the shape. The blocks are defined in a general, parameterized manner such that the model can be easily modified in the future.
The primary focus of this work is the development of methods for generating assembly sequences in a time-feasible manner that ensure static stability at each step of the assembly. Most existing research focuses on complete enumeration of every possible assembly sequence and evaluation of many possible sequences. This, however, is not practical for systems with a large number of parts for two reasons: (1) the number of possible assembly sequences is exponential in the number of parts, and (2) each static stability test is very time-consuming. The approach proposed here is to develop a multi-hierarchical rule-based approach to assembly sequences. This is accomplished by formalizing and justifying both high-level and mid-level assembly rules based on static considerations.
Application of these rules helps develop assembly sequences rapidly. The assembly sequence is developed in a time-feasible manner according to the geometry of the structure, rather than evaluating statics along the way. This work only evaluates the static stability of each step of the assembly once. The behavior of the various rules is observed both numerically and through theory, and guidelines are developed to suggest which rules to apply.
A secondary focus of this work is to introduce methods by which the inside of the structure can be optimized. This structure optimization research is implemented by genetic algorithms that solve the multi-objective optimization problem in two dimensions, and can be extended to three dimensions.
Advisors/Committee Members: Ebert-Uphoff, Imme (Committee Chair), Lipkin, Harvey (Committee Co-Chair), Balch, Tucker (Committee Member), Book, Wayne (Committee Member), Surovek, Andrea (Committee Member).